Stable Boundary Layer Height Parameterization: Learning from Artificial Neural Networks

Wei Li

Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, USA.

DOI: 10.4236/acs.2013.34055                

Abstract

Artificial neural networks (ANN) are employed using different combinations among the surface friction velocity u_*, surface buoyancy flux B_s, free-flow stability N, Coriolis parameter f, and surface roughness length z₀ from large-eddy simulation data as inputs to investigate which variables are essential in determining the stable boundary layer(SBL) height h. In addition, the performances of several conventional linear SBL height parameterizations are evaluated. ANN results indicate that the surface friction velocity u_* is the most predominant variable in the estimation of SBL height h. When u_* is absent, the secondly important variable is the surface buoyancy flux B_s. The relevance of N, f, and z₀ to h is also discussed; f affects more than N does, and z₀ shows to be the most insensitive variable to h.

Keywords

Artificial Neural Network; Large-Eddy Simulation; Stable Boundary Layer Height

Conflicts of Interest

The authors declare no conflicts of interest.

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